365天深度学习训练营-第P5周:运动鞋识别

  • 本文为365天深度学习训练营 中的学习记录博客
  • 参考文章:Pytorch实战 | 第P5周:运动鞋识别
  • 原作者:K同学啊|接辅导、项目定制

要求:

了解如何设置动态学习率(重点)
调整代码使测试集accuracy到达84%。

拔高(可选):

保存训练过程中的最佳模型权重
调整代码使测试集accuracy到达86%。
我的环境:
语言环境:Python3.8
编译器:Jupyter Lab
深度学习环境:Pytorch
数据集:K同学啊的百度网盘、和鲸
一、 前期准备

  1. 设置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets

import os,PIL,pathlib

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

device

如果设备上支持GPU就使用GPU,否则使用CPU
2. 导入数据

import os,PIL,random,pathlib

data_dir = './data/5-data/'
data_dir = pathlib.Path(data_dir)

data_paths  = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[2] for path in data_paths]
classeNames
# 关于transforms.Compose的更多介绍可以参考:https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
    transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
    # transforms.RandomHorizontalFlip(), # 随机水平翻转
    transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor,并归一化到[0,1]之间
    transforms.Normalize(           # 标准化处理-->转换为标准正太分布(高斯分布),使模型更容易收敛
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])

test_transform = transforms.Compose([
    transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸
    transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor,并归一化到[0,1]之间
    transforms.Normalize(           # 标准化处理-->转换为标准正太分布(高斯分布),使模型更容易收敛
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])

train_dataset = datasets.ImageFolder("./data/5-data/train/",transform=train_transforms)
test_dataset  = datasets.ImageFolder("./data/5-data/test/",transform=train_transforms)
train_dataset.class_to_idx

二、构建简单的CNN网络

batch_size = 32

train_dl = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True,
                                           num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
                                          batch_size=batch_size,
                                          shuffle=True,
                                          num_workers=1)
for X, y in test_dl:
    print("Shape of X [N, C, H, W]: ", X.shape)
    print("Shape of y: ", y.shape, y.dtype)
    break

365天深度学习训练营-第P5周:运动鞋识别_第1张图片

三、 训练模型

import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1=nn.Sequential(
            nn.Conv2d(3, 12, kernel_size=5, padding=0), # 12*220*220
            nn.BatchNorm2d(12),
            nn.ReLU())

        self.conv2=nn.Sequential(
            nn.Conv2d(12, 12, kernel_size=5, padding=0), # 12*216*216
            nn.BatchNorm2d(12),
            nn.ReLU())

        self.pool3=nn.Sequential(
            nn.MaxPool2d(2))                              # 12*108*108

        self.conv4=nn.Sequential(
            nn.Conv2d(12, 24, kernel_size=5, padding=0), # 24*104*104
            nn.BatchNorm2d(24),
            nn.ReLU())

        self.conv5=nn.Sequential(
            nn.Conv2d(24, 24, kernel_size=5, padding=0), # 24*100*100
            nn.BatchNorm2d(24),
            nn.ReLU())

        self.pool6=nn.Sequential(
            nn.MaxPool2d(2))                              # 24*50*50

        self.dropout = nn.Sequential(
            nn.Dropout(0.2))

        self.fc=nn.Sequential(
            nn.Linear(24*50*50, len(classeNames)))

    def forward(self, x):

        batch_size = x.size(0)
        x = self.conv1(x)  # 卷积-BN-激活
        x = self.conv2(x)  # 卷积-BN-激活
        x = self.pool3(x)  # 池化
        x = self.conv4(x)  # 卷积-BN-激活
        x = self.conv5(x)  # 卷积-BN-激活
        x = self.pool6(x)  # 池化
        x = self.dropout(x)
        x = x.view(batch_size, -1)  # flatten 变成全连接网络需要的输入 (batch, 24*50*50) ==> (batch, -1), -1 此处自动算出的是24*50*50
        x = self.fc(x)

        return x

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

model = Model().to(device)
model
Model(
  (conv1): Sequential(
    (0): Conv2d(3, 12, kernel_size=(5, 5), stride=(1, 1))
    (1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (conv2): Sequential(
    (0): Conv2d(12, 12, kernel_size=(5, 5), stride=(1, 1))
    (1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (pool3): Sequential(
    (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (conv4): Sequential(
    (0): Conv2d(12, 24, kernel_size=(5, 5), stride=(1, 1))
    (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (conv5): Sequential(
    (0): Conv2d(24, 24, kernel_size=(5, 5), stride=(1, 1))
    (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (pool6): Sequential(
    (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (dropout): Sequential(
    (0): Dropout(p=0.2, inplace=False)
  )
  (fc): Sequential(
    (0): Linear(in_features=60000, out_features=2, bias=True)
  )
)
  1. 编写训练函数
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)  # 训练集的大小
    num_batches = len(dataloader)   # 批次数目, (size/batch_size,向上取整)

    train_loss, train_acc = 0, 0  # 初始化训练损失和正确率

    for X, y in dataloader:  # 获取图片及其标签
        X, y = X.to(device), y.to(device)

        # 计算预测误差
        pred = model(X)          # 网络输出
        loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距,targets为真实值,计算二者差值即为损失

        # 反向传播
        optimizer.zero_grad()  # grad属性归零
        loss.backward()        # 反向传播
        optimizer.step()       # 每一步自动更新

        # 记录acc与loss
        train_acc  += (pred.argmax(1) == y).type(torch.float).sum().item()
        train_loss += loss.item()

    train_acc  /= size
    train_loss /= num_batches

    return train_acc, train_loss
  1. 编写测试函数
    测试函数和训练函数大致相同,但是由于不进行梯度下降对网络权重进行更新,所以不需要传入优化器
def test (dataloader, model, loss_fn):
    size        = len(dataloader.dataset)  # 测试集的大小
    num_batches = len(dataloader)          # 批次数目, (size/batch_size,向上取整)
    test_loss, test_acc = 0, 0

    # 当不进行训练时,停止梯度更新,节省计算内存消耗
    with torch.no_grad():
        for imgs, target in dataloader:
            imgs, target = imgs.to(device), target.to(device)

            # 计算loss
            target_pred = model(imgs)
            loss        = loss_fn(target_pred, target)

            test_loss += loss.item()
            test_acc  += (target_pred.argmax(1) == target).type(torch.float).sum().item()

    test_acc  /= size
    test_loss /= num_batches

    return test_acc, test_loss
  1. 设置动态学习率
    ✨调用官方动态学习率接口
    与上面方法是等价的
def adjust_learning_rate(optimizer, epoch, start_lr):
    # 每 2 个epoch衰减到原来的 0.98
    lr = start_lr * (0.92 ** (epoch // 2))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

learn_rate = 1e-4 # 初始学习率
optimizer  = torch.optim.SGD(model.parameters(), lr=learn_rate)
  1. 正式训练
loss_fn    = nn.CrossEntropyLoss() # 创建损失函数
epochs     = 40

train_loss = []
train_acc  = []
test_loss  = []
test_acc   = []

for epoch in range(epochs):
    # 更新学习率(使用自定义学习率时使用)
    adjust_learning_rate(optimizer, epoch, learn_rate)

    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
    # scheduler.step() # 更新学习率(调用官方动态学习率接口时使用)

    model.eval()
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)

    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)

    # 获取当前的学习率
    lr = optimizer.state_dict()['param_groups'][0]['lr']

    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')
    print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss,
                          epoch_test_acc*100, epoch_test_loss, lr))
print('Done')
Epoch: 1, Train_acc:53.8%, Train_loss:0.749, Test_acc:51.3%, Test_loss:0.719, Lr:1.00E-04
Epoch: 2, Train_acc:61.4%, Train_loss:0.659, Test_acc:63.2%, Test_loss:0.600, Lr:1.00E-04
Epoch: 3, Train_acc:64.5%, Train_loss:0.634, Test_acc:71.1%, Test_loss:0.554, Lr:9.20E-05
Epoch: 4, Train_acc:69.9%, Train_loss:0.578, Test_acc:76.3%, Test_loss:0.541, Lr:9.20E-05
Epoch: 5, Train_acc:75.3%, Train_loss:0.534, Test_acc:73.7%, Test_loss:0.517, Lr:8.46E-05
Epoch: 6, Train_acc:75.7%, Train_loss:0.505, Test_acc:77.6%, Test_loss:0.488, Lr:8.46E-05
Epoch: 7, Train_acc:78.3%, Train_loss:0.477, Test_acc:77.6%, Test_loss:0.527, Lr:7.79E-05
Epoch: 8, Train_acc:77.3%, Train_loss:0.474, Test_acc:71.1%, Test_loss:0.499, Lr:7.79E-05
Epoch: 9, Train_acc:81.5%, Train_loss:0.456, Test_acc:78.9%, Test_loss:0.465, Lr:7.16E-05
Epoch:10, Train_acc:82.5%, Train_loss:0.429, Test_acc:81.6%, Test_loss:0.478, Lr:7.16E-05
Epoch:11, Train_acc:83.7%, Train_loss:0.417, Test_acc:81.6%, Test_loss:0.450, Lr:6.59E-05
Epoch:12, Train_acc:85.1%, Train_loss:0.395, Test_acc:82.9%, Test_loss:0.481, Lr:6.59E-05
Epoch:13, Train_acc:84.3%, Train_loss:0.387, Test_acc:82.9%, Test_loss:0.449, Lr:6.06E-05
Epoch:14, Train_acc:87.5%, Train_loss:0.362, Test_acc:81.6%, Test_loss:0.455, Lr:6.06E-05
Epoch:15, Train_acc:87.5%, Train_loss:0.362, Test_acc:82.9%, Test_loss:0.436, Lr:5.58E-05
Epoch:16, Train_acc:88.6%, Train_loss:0.353, Test_acc:82.9%, Test_loss:0.431, Lr:5.58E-05
Epoch:17, Train_acc:89.2%, Train_loss:0.350, Test_acc:82.9%, Test_loss:0.419, Lr:5.13E-05
Epoch:18, Train_acc:87.8%, Train_loss:0.346, Test_acc:81.6%, Test_loss:0.404, Lr:5.13E-05
Epoch:19, Train_acc:89.8%, Train_loss:0.322, Test_acc:80.3%, Test_loss:0.443, Lr:4.72E-05
Epoch:20, Train_acc:87.3%, Train_loss:0.340, Test_acc:84.2%, Test_loss:0.452, Lr:4.72E-05
Epoch:21, Train_acc:89.2%, Train_loss:0.336, Test_acc:82.9%, Test_loss:0.434, Lr:4.34E-05
Epoch:22, Train_acc:91.8%, Train_loss:0.314, Test_acc:81.6%, Test_loss:0.436, Lr:4.34E-05
Epoch:23, Train_acc:92.4%, Train_loss:0.306, Test_acc:81.6%, Test_loss:0.402, Lr:4.00E-05
Epoch:24, Train_acc:90.6%, Train_loss:0.309, Test_acc:81.6%, Test_loss:0.445, Lr:4.00E-05
Epoch:25, Train_acc:92.6%, Train_loss:0.295, Test_acc:84.2%, Test_loss:0.377, Lr:3.68E-05
Epoch:26, Train_acc:92.4%, Train_loss:0.298, Test_acc:82.9%, Test_loss:0.412, Lr:3.68E-05
Epoch:27, Train_acc:93.2%, Train_loss:0.298, Test_acc:81.6%, Test_loss:0.389, Lr:3.38E-05
Epoch:28, Train_acc:93.4%, Train_loss:0.284, Test_acc:81.6%, Test_loss:0.426, Lr:3.38E-05
Epoch:29, Train_acc:93.2%, Train_loss:0.284, Test_acc:84.2%, Test_loss:0.405, Lr:3.11E-05
Epoch:30, Train_acc:94.0%, Train_loss:0.278, Test_acc:82.9%, Test_loss:0.452, Lr:3.11E-05
Epoch:31, Train_acc:92.8%, Train_loss:0.286, Test_acc:82.9%, Test_loss:0.419, Lr:2.86E-05
Epoch:32, Train_acc:94.4%, Train_loss:0.275, Test_acc:82.9%, Test_loss:0.434, Lr:2.86E-05
Epoch:33, Train_acc:95.2%, Train_loss:0.269, Test_acc:82.9%, Test_loss:0.385, Lr:2.63E-05
Epoch:34, Train_acc:94.2%, Train_loss:0.269, Test_acc:82.9%, Test_loss:0.412, Lr:2.63E-05
Epoch:35, Train_acc:93.2%, Train_loss:0.273, Test_acc:82.9%, Test_loss:0.426, Lr:2.42E-05
Epoch:36, Train_acc:94.2%, Train_loss:0.275, Test_acc:82.9%, Test_loss:0.386, Lr:2.42E-05
Epoch:37, Train_acc:94.4%, Train_loss:0.262, Test_acc:82.9%, Test_loss:0.393, Lr:2.23E-05
Epoch:38, Train_acc:95.2%, Train_loss:0.264, Test_acc:82.9%, Test_loss:0.375, Lr:2.23E-05
Epoch:39, Train_acc:94.2%, Train_loss:0.265, Test_acc:82.9%, Test_loss:0.422, Lr:2.05E-05
Epoch:40, Train_acc:94.6%, Train_loss:0.257, Test_acc:82.9%, Test_loss:0.379, Lr:2.05E-05
Done

四、 结果可视化
7. Loss与Accuracy图

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息
plt.rcParams['font.sans-serif']    = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号
plt.rcParams['figure.dpi']         = 100        #分辨率

epochs_range = range(epochs)

plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)

plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

output_28_0.png

365天深度学习训练营-第P5周:运动鞋识别_第2张图片

  1. 指定图片进行预测
    ⭐torch.squeeze()详解
    对数据的维度进行压缩,去掉维数为1的的维度
    函数原型:
    torch.squeeze(input, dim=None, *, out=None)
    关键参数说明:
    input (Tensor):输入Tensor
    dim (int, optional):如果给定,输入将只在这个维度上被压缩
    实战案例:
    ⭐torch.unsqueeze()
    对数据维度进行扩充。给指定位置加上维数为一的维度
    函数原型:
    torch.unsqueeze(input, dim)
    关键参数说明:
    input (Tensor):输入Tensor
    dim (int):插入单例维度的索引
    实战案例:
from PIL import Image

classes = list(train_dataset.class_to_idx)

def predict_one_image(image_path, model, transform, classes):

    test_img = Image.open(image_path).convert('RGB')
    # plt.imshow(test_img)  # 展示预测的图片

    test_img = transform(test_img)
    img = test_img.to(device).unsqueeze(0)

    model.eval()
    output = model(img)

    _,pred = torch.max(output,1)
    pred_class = classes[pred]
    print(f'预测结果是:{pred_class}')
# 预测训练集中的某张照片
predict_one_image(image_path='./5-data/test/adidas/1.jpg', 
                  model=model, 
                  transform=train_transforms, 
                  classes=classes)

365天深度学习训练营-第P5周:运动鞋识别_第3张图片

五、保存并加载模型

# 模型保存
PATH = './model.pth'  # 保存的参数文件名
torch.save(model.state_dict(), PATH)

# 将参数加载到model当中
model.load_state_dict(torch.load(PATH, map_location=device))

六、动态学习率
9. torch.optim.lr_scheduler.StepLR
等间隔动态调整方法,每经过step_size个epoch,做一次学习率decay,以gamma值为缩小倍数。
函数原型:
torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)
关键参数详解:
optimizer(Optimizer):是之前定义好的需要优化的优化器的实例名
step_size(int):是学习率衰减的周期,每经过每个epoch,做一次学习率decay
gamma(float):学习率衰减的乘法因子。Default:0.1
用法示例:

optimizer = torch.optim.SGD(net.parameters(), lr=0.001 )
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
  1. lr_scheduler.LambdaLR
    根据自己定义的函数更新学习率。
    函数原型:
    torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1, verbose=False)
    关键参数详解:
    optimizer(Optimizer):是之前定义好的需要优化的优化器的实例名
    lr_lambda(function):更新学习率的函数
    用法示例:
lambda1 = lambda epoch: (0.92 ** (epoch // 2) # 第二组参数的调整方法
optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1) #选定调整方法             
  1. lr_scheduler.MultiStepLR
    在特定的 epoch 中调整学习率
    函数原型:
    torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1, verbose=False)
    关键参数详解:
    optimizer(Optimizer):是之前定义好的需要优化的优化器的实例名
    milestones(list):是一个关于epoch数值的list,表示在达到哪个epoch范围内开始变化,必须是升序排列
    gamma(float):学习率衰减的乘法因子。Default:0.1
    用法示例:
    Python
    复制代码
    1
    2
    3
    4
    optimizer = torch.optim.SGD(net.parameters(), lr=0.001 )
    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
    milestones=[2,6,15], #调整学习率的epoch数
    gamma=0.1)
    更多的官方动态学习率设置方式可参考:https://pytorch.org/docs/stable/optim.html
    调用官方接口示例:

model = [Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = SGD(model, 0.1)
scheduler = ExponentialLR(optimizer, gamma=0.9)

for epoch in range(20):
    for input, target in dataset:
        optimizer.zero_grad()
        output = model(input)
        loss = loss_fn(output, target)
        loss.backward()
        optimizer.step()
    scheduler.step()

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